TopicCAT: Unsupervised Topic-Guided Co-Attention Transformer for Extreme Multimodal Summarisation

The exponential growth of multimedia data has sparked a surge of interest in multimodal summarisation with multimodal output (MSMO). A relatively unexplored but essential task within this field is extreme multimodal summarisation, a process that involves creating extremely concise multimodal summaries to further address the issue of multimedia information overload. In this study, we propose a novel Unsupervised Topic-guided Co-Attention Transformer (TopicCAT) neural network to produce extreme multimodal summaries for video-document pairs. The approach consists of two learning stages for a comprehensive multimodal understanding, guided by topic-based insights: a unimodal learning stage and a cross-modal learning stage, in which a cross-modal topic model is devised to capture the overarching themes present in both documents and videos. To achieve unsupervised learning, eliminating the need for resource-expensive collection of ground-truth multimodal summaries, we propose an optimal transport-based optimisation scheme to evaluate summary coverage from a semantic distribution perspective at the topic-level. Comprehensive experiments demonstrate the effectiveness of our proposed TopicCAT method on a multimodal news dataset, achieving a BERTScore of 84.46 and an accuracy of 0.60.